In modern homogeneous and heterogeneous cellular network topologies, large numbers of base stations, operating on similar licensed frequency spectra, are being utilized by network access providers to accommodate a growing demand for increased network capacity. In networks where neighboring network cells have overlapping wireless coverage areas, it is particularly important for service providers to be able to accurately determine which network communications resources should be allocated during various scheduling tasks in order to most efficiently facilitate communications for network service subscribers located within the overlapping regions. Generally, subscriber diversity in these cell areas can lead to unique network resource consumption and co-channel interference patterns. Traffic densities in these localities may vary widely throughout the course of any particular day, on a time-varying basis. Accordingly, compensating for these phenomena becomes more challenging as wireless communications technologies evolve in response to increased consumer demand.
Today, commercial cellular deployments are utilized to provide a larger breadth of digital communications services to varying types of distributed network clientele communicating with both dated and cutting-edge wireless computing devices. For example, many users residing within metropolitan regions of a cellular network have access to relatively high network throughput service. This service may be associated with enhanced data-rate plans that can include high bandwidth service offerings. Relatively high usage subscribers (e.g., those consuming a disproportionate percentage of available network bandwidth) may utilize local network resources to transfer large amounts of Internet-based data to and from their cellular communications device(s) over the course of a single day. Conversely, other wireless subscribers, with lesser network service (e.g., those with lower bandwidth data rate plans or dated communications devices), may use local network resources primarily for voice data communications. As would be understood by those skilled in the Art, network throughput is generally defined as an average rate of successful data communications delivery over a particular network communications channel per unit of time. This throughput is usually measured in bits per second (bps) or alternately in data packets per second. Generally, service providers wish to maximize network throughput to ensure that they can reliably accommodate consumer demand in accordance with defined communications quality levels.
As the number of active users in a particular wireless communications network increases, the problem of intercell interference (co-channel interference amongst neighboring network cells) increases, and it becomes more and more important for service providers to be able to properly manage radio frequency resources that are shared amongst regional network cells, particularly in networks employing frequency reuse assignment. By way of example, neighboring cells having overlapping coverage areas might share a fixed number of wireless communication channels, and on any given day, a particular network cell may experience detrimentally reduced network capacity and/or quality, based on heavy subscriber usage of its limited, available network resources (e.g., available communications channel bandwidth) and on intercell interference emanating from neighboring cells. Generally, intercell interference most significantly impacts users communicating near the edge or periphery of a serving cells coverage area.
Modern channel allocation schemes generally allocate full downlink transmit power to distributed user equipment, regardless of their position within a serving cell. Accordingly, these resource allocation schemes fail to adequately account for the conservative, sufficient power levels actually required to successfully close radio links between base stations and their locally served user equipment. Systems such as these generally operate in either full power mode or zero power mode, without any power scaling mechanism or dimension of power weighting. In this environment, scheduled channel resources that facilitate user equipment communications are generally either ON or OFF. When full power mode is employed in adjacent edge regions of neighboring cells, power levels may generally be considered to be overpowered. A problem arises when a serving cell's neighbor cells employ the same overpowered scheduling technique. In this scenario, uncoordinated, non-weighted scheduling gives rise to the problem of co-channel interference and it typically results in wasted network resources and decreased communications throughput.
In recent years, OFDMA (orthogonal frequency division multiple access) has emerged as an evolving physical layer technology for 4G wireless networks. 4G wireless networks have created an increased demand for higher system capacity and improved QoS, and as a result, the problem of poor cell edge performance due to co-channel intercell interference has become an even larger problem than it was for 3G and 3GPP LTE networks. In modern wireless cellular communication systems, cell edge users (users having low carrier to interference plus noise ratio or CINR) regularly suffer from severe intercell interference, and as a result, they generally achieve far lower throughput than users located in the central regions of a network cell (users having high CINR). This not only degrades overall system throughput, but it also causes a wide variation in the QoS levels among varying user types residing in different regions of a serving network cell.
As previously discussed, cell power scheduling in modern OFDMA systems is most often employed independently of similar scheduling performed at neighboring network cells. What is needed are improved solutions for coordinating power scheduling (particularly on the downlink) amongst neighboring network cells to effectively reduce the impact of inter-cell interference and to improve cell edge performance for peripheral network users. Several interference mitigation solutions have been proposed in an attempt to solve these problems. Unfortunately, these solutions have inherent deficiencies that hinder system performance and/or efficient network resource utilization.
One previously proposed solution is intercell interference randomization. This technique essentially randomizes interfering signals, and thereby facilitates interference suppression. As would be understood by those skilled in the Art, this approach may include: interleave division multiple access and slow frequency hopping. These techniques merely randomize intercell interference into noise and accordingly intercell interference randomization techniques fail to achieve substantial performance improvement. Another proposed solution is interference cancellation. This technique demodulates and cancels interference via multi-user detection methods at the receiver. However, these techniques generally suffer from high complexity and detrimentally increased consumer resource overhead. As a result, from a practical perspective, this solution can only result in a limited amount of interference being cancelled in a typical wireless communications network. Accordingly, the effect of interference cancellation alone is insufficient as it cannot solve intercell interference problems associated with modern cellular networks.
Another proposed solution is a type of interference coordination, known as fractional frequency reuse (FFR). FFR aims at using orthogonal frequency resources among neighboring cells' edge users to actively mitigate intercell interference. Implementation of this approach has a low complexity and FFR can improve performance. However, FFR has several key deficiencies. Depending on the specific implementation, since a cell-edge user can only use part of a frequency band, the user can suffer from loss of frequency selectivity gain. Additionally, since FFR schemes are in general statically configured, they do not react to networks with non-uniform loading across the network. This generally leads to a non-optimal system throughput. As would be understood by those skilled in the Art, there are also several other inherent problems associated with using FFR as a solution for modern intercell interference problems that make alternative solutions desirable.
As existing intercell interference solutions fail to adequately solve the problem of intercell interference amongst neighboring network cells, it would be beneficial to be able to more efficiently allocate network resources amongst nearby cells having overlapping coverage areas. This would help to improve network resource utilization amongst regional network cells and it would also improve QoS levels experienced by users communicating at a particular cell's edge. Negative effects associated with poor QoS (e.g., conditions commonly caused by co-channel interference), which can be mitigated by optimizing network resource allocation using improved network resource scheduling, may include: queuing delay, data loss, as well as blocking of new and existing network connections for certain network subscribers.
Accordingly, there remains a need for systems and methods that employ improved network resource allocation solutions to better compensate for intercell interference problems amongst neighboring network cells. It would be helpful if these solutions offered robust power scheduling solutions that emphasized coordinated scheduling while requiring minimal operational overhead. In this way, it would be easier for service providers to readily allocate network resources to network service subscribers in a time efficient manner, in the presence of dynamically changing network environments. It would also be helpful if these solutions took advantage of existing network resources, such that various network cells could independently determine their own downlink data schedules with minimal input from neighboring cells and/or centralized controlling entities. These improved network optimization solutions would effectively reduce the level of required human intervention for successful network resource allocation operations. This in turn would result in operational savings for service providers, and it would provide for many other performance, quality, and operational benefits. The importance of these benefits would be readily understood by those familiar with the multitude of benefits commonly associated with self-organized network (SoN) solutions.